Investigation of incidents is an important consideration in large scale video surveillance systems, such as those used by city authorities or law enforcement to monitor and investigate traffic incidents. These large scale surveillance systems often need to track objects across several different sensors, while understanding the connection between the tracked objects in the different sensors.
Various solutions to the problem of connecting tracked objects in different sensors have been proposed in the prior art. For example:
PCT Patent Publication No. WO2014/072971 describes determining a license plate number of a vehicle tracked by a surveillance system by monitoring a first area with a surveillance camera to detect entry of a vehicle into the first area and recording a detection time, and substantially simultaneously capturing with a LPR camera an image of a license plate of a vehicle entering the first area, and correlating the time of the detection with the time of the capture to associate the tracked vehicle with a license plate number.
U.S. Pat. No. 5,696,503 describes a traffic surveillance system having a plurality of sensor systems separated by a roadway link, each sensor system comprising a fingerprinting sensor and a traffic processor, the sensor providing raw signals including fingerprints of vehicles within a field, and the processor distinguishing individual vehicles based upon their respective fingerprints, reducing the fingerprints to characterizations of predefined attributes, and determining the position of each distinguished vehicle within the field.
U.S. Pat. No. 7,295,106 describes classifying objects in a monitored zone using multiple surveillance devices by receiving a set of objects within a predefined zone area from each of at least a first and second surveillance means. Subsequently, each received set of objects is filtered to ensure that the objects in the set are comparable to the objects in the other received set. Characteristics of the received sets of objects are compared and characteristics of the objects within a received set of objects are compared to characteristics of the objects within a different set of received objects, wherein the characteristics are based upon a set of predetermined characteristics. It is determined if each object or set identified by the first surveillance means corresponds to an object or set identified by the second surveillance means.
U.S. Patent Publication No. 2014/0098221 describes an approach for re-identifying, in a second test image, an object in a first test image by determining a brightness transfer functions (BTFs) between respective pairs of training images. Respective similarity measures are determined between the first test image and each of the training images captured by the first camera (first training images). A weighted brightness transfer function (WBTF) is determined by combining the BTFs weighted by weights of the first training images. The first test image is transformed by the WBTF to better match one of the training images captured by the second camera. Another test image, captured by the second camera, is identified because it is closer in appearance to the transformed test image than other test images captured by the second camera.
EP Patent No. 1,489,552 describes improved detection and recognition of objects such as vehicles by image processing a camera image of the vehicle by correcting sensor pixel values with a reflection component. The detected vehicle can be re-identified downstream by a second camera. The success rate of recognition can be improved by recognizing additional object consequences and/or platoons.
PCT Patent Publication No. WO2011/120194 describes measuring a journey time between nodes in a road network by detecting characteristics of a car sequence sequentially passing through the node network, wherein a first node reports characteristics of the car sequence to a neighbor node, and the neighbor node compares the characteristics of the car sequence reported by the first node with characteristics of the car sequence detected at the neighbor node to find a matching position, and calculates a journey time from the first node to the neighbor node.
Coifman, Benjamin (1999), “Vehicle Reidentification and Travel Measurements on Congested Freeways”, California Partners for Advanced Transit and Highways (PATH), describes using loop detectors at an upstream and downstream location to measure vehicle lengths, and comparing vehicle platoons detected at the upstream and downstream locations to identify matching platoons based on the vehicle lengths of vehicles in the platoons, thereby enabling identification of a particular vehicle of particular length within the reidentified platoon.
C. C. Sun, R. P. Ramachandran and S. G. Ritchie “Vehicle reidentification using multidetector fusion”, IEEE Trans. Intell. Transp. Syst., vol. 5, no. 3, pp. 155-164 2004, describes a multi-detector vehicle re-identification algorithm by selecting a platoon detected at a downstream site, generating a list of upstream candidate platoons subject to a time window constraint, and comparing each upstream platoon to the downstream platoon using feature vectors and a linear L1 (absolute distance) nearest neighbor classifier to determine a best matching platoon, whereupon individual vehicles are then reidentified.
The references cited below teach background information that may be applicable to the presently disclosed subject matter:
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The full contents of the above publications are incorporated by reference herein in their entirety.